U.S. patent application number 12/766535 was filed with the patent office on 2010-11-04 for apparatus, method, and program for processing information.
Invention is credited to Mitsuhiro MIYAZAKI.
Application Number | 20100281497 12/766535 |
Document ID | / |
Family ID | 43019562 |
Filed Date | 2010-11-04 |
United States Patent
Application |
20100281497 |
Kind Code |
A1 |
MIYAZAKI; Mitsuhiro |
November 4, 2010 |
APPARATUS, METHOD, AND PROGRAM FOR PROCESSING INFORMATION
Abstract
An information processing apparatus includes: importance
calculation means configured to calculate an experience importance
degree of content for a user based the basis of a relationship
among a reaction analysis result obtained by performing
predetermined analysis on a reaction (attitude) of the user to the
content at the time when the user experiences the content, an
environment analysis result obtained by performing predetermined
analysis on an external environment of the user at the time when
the user experiences the content, a signal analysis result obtained
by performing predetermined analysis on a video signal or voice
signal of the content, and a language analysis result obtained by
performing predetermined language analysis on language information
described about the content; and determination means configured to,
if the experience importance degree of the content calculated by
the importance calculation means is high, determine the content as
recommendation content to be recommended to the user.
Inventors: |
MIYAZAKI; Mitsuhiro;
(Kanagawa, JP) |
Correspondence
Address: |
FINNEGAN, HENDERSON, FARABOW, GARRETT & DUNNER;LLP
901 NEW YORK AVENUE, NW
WASHINGTON
DC
20001-4413
US
|
Family ID: |
43019562 |
Appl. No.: |
12/766535 |
Filed: |
April 23, 2010 |
Current U.S.
Class: |
725/14 ;
704/8 |
Current CPC
Class: |
H04N 21/44218 20130101;
H04N 21/422 20130101; G06F 16/436 20190101; H04N 21/4394 20130101;
H04N 21/8405 20130101; G06F 16/4387 20190101; G06F 16/437 20190101;
H04N 21/44008 20130101; H04N 5/76 20130101; H04N 21/4668 20130101;
H04N 21/84 20130101 |
Class at
Publication: |
725/14 ;
704/8 |
International
Class: |
H04N 7/16 20060101
H04N007/16; G06F 17/20 20060101 G06F017/20 |
Foreign Application Data
Date |
Code |
Application Number |
May 1, 2009 |
JP |
P2009-112016 |
Claims
1. An information processing apparatus comprising: importance
degree calculation means configured to calculate an experience
importance degree of content for a user on the basis of a
relationship among a reaction analysis result obtained by
performing a predetermined analysis on a reaction of the user to
the content at the time when the user experiences the content, the
reaction being an attitude of the user, an environment analysis
result obtained by performing a predetermined analysis on an
external environment of the user at the time when the user
experiences the content, a signal analysis result obtained by
performing a predetermined analysis on a video signal or voice
signal of the content, and a language analysis result obtained by
performing a predetermined language analysis on language
information described about the content; and determination means
configured to, if the experience importance degree of the content
calculated by the importance degree calculation means is high,
determine the content as recommendation content to be recommended
to the user.
2. The information processing apparatus according to claim 1,
further comprising relationship degree calculation means configured
to calculate a relationship degree indicating a degree of the
relationship among the reaction analysis result, the environment
analysis result, the signal analysis result, and the language
analysis result, wherein the importance degree calculation means
calculates the experience importance degree by performing a
predetermined operation on the relationship degree calculated by
the relationship degree calculation means.
3. The information processing apparatus according to claim 2,
wherein the relationship degree calculation means calculates the
relationship degree by performing a feature quantity analysis or
language analysis on the reaction analysis result, the environment
analysis result, the signal analysis result, and the language
analysis result.
4. The information processing apparatus according to claim 2,
further comprising sorting means configured to sort pieces of
content experienced by the user in accordance with corresponding
experience importance degrees calculated by the importance degree
calculation means, wherein the determination means determines, as
the recommendation content, a piece of content having a high
experience importance degree among the pieces of content sorted by
the sorting means.
5. The information processing apparatus according to claim 4,
wherein the sorting means sorts the pieces of content in the
descending order of the corresponding experience importance
degrees, and the determination means determines, as the
recommendation content, a piece of content having an experience
importance degree higher than a predetermined threshold among the
pieces of content sorted by the sorting means.
6. The information processing apparatus according to claim 4,
wherein the sorting means sorts the pieces of content in the
descending order of the corresponding experience importance
degrees, and the determination means determines, as the
recommendation content, pieces of content corresponding to highest
n experience importance degrees among the pieces of content sorted
by the sorting means.
7. The information processing apparatus according to claim 4,
further comprising retrieval means configured to retrieve a piece
of content from among the pieces of content on the basis of one or
both of a reaction analysis result and an environment analysis
result performed after the pieces of content are experienced,
wherein the sorting means sorts pieces of content retrieved by the
retrieval means in accordance with corresponding experience
importance degrees.
8. An information processing method comprising the steps of:
calculating an experience importance degree of content for a user
on the basis of a relationship among a reaction analysis result
obtained by performing a predetermined analysis on a reaction of
the user to the content at the time when the user experiences the
content, the reaction being an attitude of the user, an environment
analysis result obtained by performing a predetermined analysis on
an external environment of the user at the time when the user
experiences the content, a signal analysis result obtained by
performing a predetermined analysis on a video signal or voice
signal of the content, and a language analysis result obtained by
performing a predetermined language analysis on language
information described about the content; and if the experience
importance degree of the content calculated in the experience
importance degree calculation step is high, determining the content
as recommendation content to be recommended to the user.
9. A program causing a computer to perform a process comprising the
steps of: calculating an experience importance degree of content
for a user on the basis of a relationship among a reaction analysis
result obtained by performing a predetermined analysis on a
reaction of the user to the content at the time when the user
experiences the content, the reaction being an attitude of the
user, an environment analysis result obtained by performing a
predetermined analysis on an external environment of the user at
the time when the user experiences the content, a signal analysis
result obtained by performing a predetermined analysis on a video
signal or voice signal of the content, and a language analysis
result obtained by performing a predetermined language analysis on
language information described about the content; and if the
experience importance degree of the content calculated in the
experience importance degree calculation step is high, determining
the content as recommendation content to be recommended to the
user.
10. An information processing apparatus comprising: an importance
degree calculation unit configured to calculate an experience
importance degree of content for a user on the basis of a
relationship among a reaction analysis result obtained by
performing a predetermined analysis on a reaction of the user to
the content at the time when the user experiences the content, the
reaction being an attitude of the user, an environment analysis
result obtained by performing a predetermined analysis on an
external environment of the user at the time when the user
experiences the content, a signal analysis result obtained by
performing a predetermined analysis on a video signal or voice
signal of the content, and a language analysis result obtained by
performing a predetermined language analysis on language
information described about the content; and a determination unit
configured to, if the experience importance degree of the content
calculated by the importance degree calculation unit is high,
determine the content as recommendation content to be recommended
to the user.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention relates to an apparatus, a method, and
a program for processing information and in particular to an
apparatus, a method, and a program for processing information that
can recommend optimum content corresponding to the situation of the
user.
[0003] 2. Related Art
[0004] Related-art content recommendation systems retrieve and
recommend content on the basis of a keyword inputted by the user or
meta-data selected by the user on web pages or audio/visual (AV)
apparatuses.
[0005] Incidentally, there has been proposed a technology that
stores, as episodes, the date and time when a tune has been
outputted in the past, the place where the tune has been outputted,
the name of the tune, the apparatus that has outputted the tune,
the accompanying person, and the like and, when the tune is
retrieved later, outputs the episodes as voices or character
strings (see Japanese Unexamined Patent Application Publication No.
2006-252758).
SUMMARY OF THE INVENTION
[0006] Unfortunately, the related-art content recommendation
systems do not recommend content corresponding to the situation of
the user, that is, content corresponding to an episode that was
particularly impressive for the user.
[0007] Accordingly, it is desirable to recommend optimum content
corresponding to the situation of the user on the basis of
information about a reaction of the user shown when the user viewed
and listened to content in the past and the user's surroundings at
that time and information about the content itself.
[0008] An information processing apparatus according to an
embodiment of the present invention includes: importance
calculation means configured to calculate an experience importance
degree of content for a user on the basis of a relationship among a
reaction analysis result obtained by performing a predetermined
analysis on a reaction of the user to the content at the time when
the user experiences the content, the reaction being an attitude of
the user, an environment analysis result obtained by performing a
predetermined analysis on an external environment of the user at
the time when the user experiences the content, a signal analysis
result obtained by performing a predetermined analysis on a video
signal or voice signal of the content, and a language analysis
result obtained by performing a predetermined language analysis on
language information described about the content; and determination
means configured to, if the experience importance degree of the
content calculated by the importance calculation means is high,
determine the content as recommendation content to be recommended
to the user.
[0009] The information processing apparatus may further include
relationship degree calculation means configured to calculate a
relationship degree indicating a degree of a relationship among the
reaction analysis result, the environment analysis result, the
signal analysis result, and the language analysis result. The
importance degree calculation means may calculate the experience
importance degree by performing a predetermined operation on the
relationship degree calculated by the relationship degree
calculation means.
[0010] The relationship degree calculation means may calculate the
relationship degree by performing a feature quantity analysis or
language analysis on the reaction analysis result, the environment
analysis result, the signal analysis result, and the language
analysis result.
[0011] The information processing apparatus may further include
sorting means configured to sort pieces of content experienced by
the user in accordance with corresponding experience importance
degrees calculated by the importance degree calculation means. The
determination means may determine, as the recommendation content, a
piece of content having a high experience importance degree among
the pieces of content sorted by the sorting means.
[0012] The determination means may determine, as the recommendation
content, content having an importance degree higher than a
predetermined threshold among the pieces of content sorted by the
sorting means.
[0013] The sorting means may sort the pieces of content in the
descending order of the corresponding experience importance
degrees, and the determination means may determine, as the
recommendation content, a piece of content having an experience
importance degree higher than a predetermined threshold among the
pieces of content sorted by the sorting means.
[0014] The sorting means may sort the pieces of content in the
descending order of the corresponding experience importance
degrees, and the determination means may determine, as the
recommendation content, pieces of content corresponding to highest
n experience importance degrees among the pieces of content sorted
by the sorting means.
[0015] The information processing apparatus may further include
retrieval means configured to retrieve a piece of content from
among the pieces of content on the basis of one or both of a
reaction analysis result and an environment analysis result
performed after the pieces of content are experienced. The sorting
means may sort pieces of content retrieved by the retrieval means
in accordance with corresponding experience importance degrees.
[0016] An information processing method including the steps of:
calculating an experience importance degree of content for a user
on the basis of a relationship among a reaction analysis result
obtained by performing a predetermined analysis on a reaction of
the user to the content at the time when the user experiences the
content, the reaction being an attitude of the user, an environment
analysis result obtained by performing a predetermined analysis on
an external environment of the user at the time when the user
experiences the content, a signal analysis result obtained by
performing a predetermined analysis on a video signal or voice
signal of the content, and a language analysis result obtained by
performing a predetermined language analysis on language
information described about the content; and if the experience
importance degree of the content calculated in the experience
importance degree calculation step is high, determining the content
as recommendation content to be recommended to the user.
[0017] A program causing a computer to perform a process including
the steps of: calculating an experience importance degree of
content for a user on the basis of a relationship among a reaction
analysis result obtained by performing a predetermined analysis on
a reaction of the user to the content at the time when the user
experiences the content, the reaction being an attitude of the
user, an environment analysis result obtained by performing a
predetermined analysis on an external environment of the user at
the time when the user experiences the content, a signal analysis
result obtained by performing a predetermined analysis on a video
signal or voice signal of the content, and a language analysis
result obtained by performing a predetermined language analysis on
language information described about the content; and if the
experience importance degree of the content calculated in the
experience importance degree calculation step is high, determining
the content as recommendation content to be recommended to the
user.
[0018] According to an embodiment of the present invention, an
experience importance degree of content for a user is calculated on
the basis of a relationship among a reaction analysis result
obtained by performing a predetermined analysis on a reaction of
the user to the content at the time when the user experiences the
content, the reaction being an attitude of the user, an environment
analysis result obtained by performing a predetermined analysis on
an external environment of the user at the time when the user
experiences the content, a signal analysis result obtained by
performing a predetermined analysis on a video signal or voice
signal of the content, and a language analysis result obtained by
performing a predetermined language analysis on language
information described about the content, and if the calculated
experience importance degree of the content is high, the content is
determined as recommendation content to be recommended to the
user.
[0019] The embodiments of the present invention allow recommending
optimum content corresponding to the situation of the user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] FIG. 1 is a block diagram showing an example configuration
of an information processing system according to an embodiment of
the present invention;
[0021] FIG. 2 is a flowchart showing an information integration
process that the information processing system performs;
[0022] FIG. 3 is a flowchart showing a reaction analysis process
that a reaction analysis unit performs;
[0023] FIG. 4 is a flowchart showing an environment analysis
process that an environment analysis unit performs;
[0024] FIG. 5 is a flowchart showing a signal analysis process that
a signal analysis unit performs;
[0025] FIG. 6 is a flowchart showing a language analysis process
that a language analysis unit performs;
[0026] FIG. 7 is a flowchart showing an information integration
process that an information integration unit performs;
[0027] FIG. 8 is a flowchart showing a content recommendation
process that the information processing system performs;
[0028] FIG. 9 is a table showing an example of the list of pieces
of content retrieved by a retrieval unit; and
[0029] FIG. 10 is a diagram showing an example configuration of a
general-purpose computer.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0030] Now, an embodiment of the present invention will be
described with reference to the accompanying drawings.
[0031] Example Configuration of Information Processing System
[0032] FIG. 1 shows an example configuration of an information
processing system according to an embodiment of the present
invention. The information processing system of FIG. 1 handles
content. The "content" here refers to what are produced through
humans' creative activities. Examples of content include
characters, drawings, colors, voices, motions, images or
combinations thereof, of movies, music, plays, literature,
photographs, comics, animation, computer games, and others, and
programs for providing information related to these items via a
computer. In this specification, so-called "content data," that is,
what are produced through humans' creative activities and are
processible by apparatuses, for example, electrical signals or what
is stored in a memory is also referred to as content without being
distinguished.
[0033] The information processing system of FIG. 1 includes a
reaction analysis unit 11, an environment analysis unit 12, a
signal analysis unit 13, a language analysis unit 14, a content
management unit 15, an information integration unit 16, a content
experience unit 17, and an integration control unit.
[0034] The reaction analysis unit 11 includes a mouse, a keyboard,
a remote controller, a voice recognition apparatus, an image
recognition apparatus, a biosensor, and the like. The reaction
analysis unit 11 detects information, such as an input using a
pointing device, a voice, an image, or a biological reaction, which
is a reaction (attitude) of the user at the time when the user
views and listens to content. The reaction analysis unit 11
includes a reaction analysis detection unit 21, a reaction analysis
input unit 22, a reaction analysis storage unit 23, and a reaction
analysis operation unit 24.
[0035] The reaction analysis detection unit 21 is composed of, for
example, a voice recognition apparatus, an image recognition
apparatus, and a biosensor and detects or determines, in real time,
information corresponding to the voice, face image, and biological
reaction of the user at the time when the user views and listens to
content and provides the information to the reaction analysis
storage unit 23.
[0036] The reaction analysis input unit 22 is composed of, for
example, a pointing device, such as a mouse, keyboard, or remote
controller, and provides information corresponding to an input made
by the user using the pointing device when the user views and
listens to content, to the reaction analysis storage unit 23.
[0037] The reaction analysis storage unit 23 chronologically stores
information corresponding to the voice, image, and biological
reaction of the user from the reaction analysis detection unit 21
and information corresponding to the input using the pointing
device from the reaction analysis input unit 22 (hereafter both
referred to as "reaction information"). The reaction analysis
storage unit 23 also stores an analysis result provided by the
reaction analysis operation unit 24.
[0038] The reaction analysis operation unit 24 reads pieces of
reaction information stored in the reaction analysis storage unit
23 in turn while controlling the operation of the reaction analysis
detection unit 21, and performs a predetermined data analysis on
each read reaction information and provides the obtained analysis
result (hereafter referred to as a "reaction analysis result") to
the information integration unit 16. The reaction analysis
operation unit 24 also provides the obtained reaction analysis
result to the reaction analysis storage unit 23 to store it.
[0039] The environment analysis unit 12 includes a radio wave
clock, a global positioning system (GPS) receiver, a temperature
sensor, and the like. The environment analysis unit 12 detects
information about the external environment of the user at the time
when the user views and listens to content, such as the date and
time, place, or temperature, and user's other surroundings. The
environment analysis unit 12 includes an environment analysis
detection unit 31, an environment analysis storage unit 32, and an
environment analysis operation unit 33.
[0040] The environment analysis detection unit 31 is composed of,
for example, a radio wave clock, a GPS receiver, a temperature
sensor, and the like and detects or determines information about
the date and time, place, temperature, and the like at the time
when the user views and listens to content and user's other
surroundings (hereafter referred to as "environment information")
in real time and provides the information to the environment
analysis storage unit 32.
[0041] The environment analysis storage unit 32 chronologically
stores pieces of environment information from the environment
analysis detection unit 31. The environment analysis storage unit
32 also stores analysis results provided by the environment
analysis operation unit 33.
[0042] The environment analysis operation unit 33 reads pieces of
environment information stored in the environment analysis storage
unit 32 in turn, performs a predetermined data analysis on each
read environment information, and provides the obtained analysis
result (hereafter referred to as an "environment analysis result")
to the information integration unit 16, as well as provides the
result to the environment analysis storage unit 32 to store it.
[0043] As seen, the reaction analysis unit 11 and environment
analysis unit 12 perform an analysis on information about the
situation of the user himself/herself or user's surroundings at the
time when the user views and listens to content. Hereafter, the
obtained reaction and environment analysis results may be
collectively referred to as a "user situation analysis result" if
necessary.
[0044] The signal analysis unit 13 is composed of, for example, a
server or client computer or a software module and performs a
signal analysis on image signals and voice signals of content. The
signal analysis unit 13 includes a signal analysis storage unit 41
and a signal analysis operation unit 42.
[0045] The signal analysis storage unit 41 stores (retains) content
provided by the content management unit 15 via the information
integration unit 16. The signal analysis storage unit 41 also
stores analysis results provided by the signal analysis operation
unit 42.
[0046] The signal analysis operation unit 42 reads content stored
in the signal analysis storage unit 41, extracts image signals and
voice signals from the content, and performs a predetermined signal
analysis on these signals. The signal analysis operation unit 42
provides the result of the signal analysis to the information
integration unit 16, as well as provides the result to the signal
analysis storage unit 41 to store it.
[0047] The language analysis unit 14 is composed of, for example, a
server or client computer or a software module and performs a
predetermined language analysis on language information such as
sentences or words written about content. The language analysis
unit 14 includes a language analysis storage unit 51 and a language
analysis operation unit 52.
[0048] The language analysis storage unit 51 stores language
information provided by the content management unit 15 via the
information integration unit 16. The language information here is
meta-data about content, such as sentences or words written about
content. The language analysis storage unit 51 also stores analysis
results provided by the language analysis operation unit 52.
[0049] The language analysis operation unit 52 reads
content-related language information stored in the language
analysis storage unit 51 and performs a predetermined language
analysis on the read information. The language analysis operation
unit 52 provides the result of the language analysis to the
information integration unit 16, as well as provides the result to
the language analysis storage unit 51 to store it.
[0050] As seen, the signal analysis unit 13 and language analysis
unit 14 perform an analysis on signals or information about
content. Hereafter, the obtained signal and language analysis
results may be collectively referred to as a "content analysis
result" if necessary.
[0051] The content management unit 15 is composed of, for example,
a broadcast apparatus, a server or client computer, or database
software and manages content. The content management unit 15
includes a content storage unit 61 and a content providing unit
62.
[0052] The content storage unit 61 stores content such as images,
e.g., moving images or still images, voices, and web pages
described above, as well as meta-data about the content.
[0053] The content providing unit 62 is composed of, for example, a
distribution server of a television broadcast system, a streaming
content server on the Internet, or the like and provides content
and meta-data thereabout retained by the content storage unit 61 to
the information integration unit 16.
[0054] The information integration unit 16 relays or integrates
various types of information in the information processing system
shown in FIG. 1. For example, the information integration unit 16
provides content from the content management unit 15 to the signal
analysis unit 13 or content experience unit 17 or provides
meta-data about content from the content management unit 15 to the
language analysis unit 14. The information integration unit 16 also
integrates analysis results from the reaction analysis unit 11,
environment analysis unit 12, signal analysis unit 13, and language
analysis unit 14 and provides the integrated results to the content
recommendation unit 18. The information integration unit 16
includes an information integration control unit 71 and an
information integration storage unit 72.
[0055] The information integration control unit 71 controls relay
and integration of information in the information processing system
shown in FIG. 1. For example, upon a request from the content
experience unit 17, the information integration control unit 71
provides content provided by the content management unit 15 to the
content experience unit 17. At that time, the information
integration control unit 71 provides the content provided by the
content management unit 15 to the signal analysis unit 13, as well
as provides meta-data about the content provided by the content
management unit 15 to the language analysis unit 14.
[0056] The information integration control unit 71 also obtains an
experience importance degree representing the importance degree of
viewing and listening of content by the user on the basis of the
relationship among analysis results from the reaction analysis unit
11, environment analysis unit 12, signal analysis unit 13, and
language analysis unit 14. Hereafter, viewing and listening of
content by the user will be referred to as a "content experience."
That is, the information integration control unit 71 obtains the
importance degree of a content experience. Also, upon a request
from the content experience unit 17, the information integration
control unit 71 provides information about recommendation content
(content recommendation) provided by the content recommendation
unit 18 to the content experience unit 17. The recommendation
content here refers to content to be recommended to the user.
[0057] The information integration control unit 71 includes a
relationship degree calculation unit 71a and an importance degree
calculation unit 71b.
[0058] The relationship degree calculation unit 71a calculates an
experience relationship degree indicating the degree of the
relationship among a reaction analysis result from the reaction
analysis unit 11, an environment analysis result from the
environment analysis unit 12, a signal analysis result from the
signal analysis unit 13, and a language analysis result from the
language analysis unit.
[0059] The importance degree calculation unit 71b performs a
predetermined operation on the experience relationship degree
calculated by the relationship degree calculation unit 71a,
calculates the importance degree of a content experience, and
provides the experience importance degree to the content
recommendation unit 18, as well as provides the experience
importance degree to the information integration storage unit 72 to
store it. The experience importance degree is increased as the
experience relationship degree among the analysis results is
increased. Hereafter, a content experience having a particularly
high importance degree, that is, a content experience particularly
impressive for the user will be referred to as an "episode," if
necessary.
[0060] The information integration storage unit 72 stores the
importance degree of a content experience from the information
integration control unit 71 in such a manner that the importance
degree is associated with information indicating content (e.g.,
content name) corresponding to the content experience.
[0061] The content experience unit 17 is composed of a client
computer, consumer electronics (CE) apparatus, portable information
terminal apparatus, or the like by which the user can view an
listen to content, that is, the user can experience content. The
content experience unit 17 includes a user input unit 81, a content
experience operation unit 82, a content experience storage unit 83,
and a content experience display unit 84.
[0062] The user input unit 81 is composed of input devices for
operating the content experience unit 17, such as a keyboard,
mouse, remote controller, and touch panel. Information
corresponding to an operation performed by the user on the user
input unit 81 is provided to the content experience operation unit
82. The above-mentioned reaction analysis input unit 22 may be
configured so that it also functions as the user input unit 81.
[0063] The content experience operation unit 82 provides the
information based on the user operation from the user input unit 81
to the information integration unit 16 and thus obtains information
corresponding to the information based on the user operation, from
the information integration unit 16. For example, the content
experience operation unit 82 provides information on the basis of a
user operation indicating viewing and listening of content to the
information integration unit 16 and thus obtains the content from
the content management unit 15 via the information integration unit
16 and provides the content to the content experience display unit
84. Also, for example, the content experience operation unit 82
provides information corresponding to a user operation indicating
acquisition of a content recommendation to the information
integration unit 16 and thus obtains a content recommendation from
the content management unit 18 via the information integration unit
16 and provides the recommendation to the content experience
display unit 84. The content experience operation unit 82 also
provides various types of information provided by the information
integration unit 16 to the content experience storage unit 83, as
necessary. The content experience operation unit 82 also controls
display on the content experience display unit 84. For example, the
information integration unit 82 performs control such that content
or a content recommendation from the content management unit 16 is
properly laid out on a monitor or graphic user interface (GUI)
screen serving as the content experience display unit 84.
[0064] The content experience storage unit 83 stores various types
of information provided by the content experience operation unit
82.
[0065] The content experience display unit 84 is composed of output
devices, such as a GUI screen for displaying a content
recommendation result or the like, a monitor capable of displaying
content itself, speaker, and printer. For example, under the
control of the content experience operation unit 82, the content
experience display unit 84 displays content or a content
recommendation in accordance with various types of information from
the content experience operation unit 82.
[0066] The content recommendation unit 18 is composed of, for
example, a server or client computer, software module, or the like
and recommends content to the user. The content recommendation unit
18 includes a content recommendation storage unit 91 and a content
recommendation operation unit 92.
[0067] The content recommendation storage unit 91 stores various
types of information provided by the information integration unit
16, for example, the importance degree of a content experience
indicating the result of integration of the analysis results, or
the analysis results themselves. The content recommendation storage
unit 91 also stores user preference information that the content
recommendation operation unit 92 generates on the basis of the
above-mentioned information. The user preference information here
refers to information indicating whether the user had a positive
feeling or negative feeling about content corresponding to a
content experience. The content recommendation storage unit 91 also
stores a content recommendation made by the content recommendation
operation unit 92.
[0068] The content recommendation operation unit 92 retrieves
content corresponding to an episode, which is a content experience
particularly impressive for the user, by using information stored
in the content recommendation storage unit 91, specifically, by
using the importance degree of a content experience and provides a
content recommendation to the information integration unit 16. The
content recommendation operation unit 92 also provides the
recommendation content to the content recommendation storage unit
91 to store it. The content recommendation operation unit 92 also
generates user preference information using various types of
information provided by the information integration unit 16 and
provides the user preference information to the content
recommendation storage unit 91.
[0069] The content recommendation operation unit 92 includes a
retrieval unit 92a, a sorting unit 92b, and a determination unit
92c.
[0070] In a case where the user newly experiences content, the
retrieval unit 92a retrieves content corresponding to information
inputted through a user operation from among pieces of content
corresponding to experience importance degrees in the content
recommendation storage unit 91.
[0071] The sorting unit 92b sorts pieces of content retrieved by
the retrieval unit 92a, according to corresponding content
experience importance degrees. More specifically, the sorting unit
92b sorts pieces of content retrieved by the retrieval unit 92a in
the descending order of corresponding experience importance
degrees.
[0072] The determination unit 92c determines, as recommendation
content to be recommended to the user, content corresponding to a
content experience having a high importance degree, that is,
content corresponding to an episode among the pieces of content
sorted by the sorting unit 92b and provides a content
recommendation to the information integration unit 16.
[0073] In the information processing system of FIG. 1, the reaction
analysis unit 11, environment analysis unit 12, signal analysis
unit 13, language analysis unit 14, content management unit 15,
content experience unit 17, and content recommendation unit 18 may
be connected to the information integration unit 16 in any form,
whether by wire or wirelessly, whether via the Internet or an
intranet.
[0074] Information Integration Process by Information Processing
System
[0075] Referring now to FIG. 2, an information integration process
that the information processing system of FIG. 1 performs will be
described. The information integration process is started when the
user starts to view and listen to content by operating the user
input unit 81 of the content experience unit 17.
[0076] In step S11, the reaction analysis unit 11 performs a
reaction analysis process, more specifically, performs a
predetermined data analysis on reaction information, which is
information such as an input by the user using a pointing device, a
voice, an image, a biological reaction, or the like of the user at
the time when the user views and listens to content.
[0077] Reaction Analysis Process by Reaction Analysis Unit
[0078] Referring now to FIG. 3, a reaction analysis process that
the reaction analysis unit 11 performs will be described.
[0079] In step S21, the reaction analysis unit 11 detects user
reaction information. More specifically, the reaction analysis
detection unit 21 detects or determines, in real time, information
corresponding to a voice made by the user and a face image and a
biological reaction of the user at the time when the user views and
listens to content under the control of the reaction analysis
operation unit 24 and provides the information to the reaction
analysis storage unit 23. The reaction analysis input unit 22
provides, to the reaction analysis storage unit 23, information
corresponding to an operation, such as an input using a pointing
device, performed by the user at the time when the user views and
listens to content, for example, information corresponding to an
email or diary inputted by the user at that time or a description
inputted by the user on a web page via the Internet at that
time.
[0080] In step S22, the reaction analysis storage unit 23
chronologically stores the above-mentioned pieces of reaction
information, that is, the information corresponding to the voice,
image, and biological reaction of the user provided by the reaction
analysis detection unit 21 and the information corresponding to the
input using the pointing device provided by the reaction analysis
input unit 22.
[0081] In step S23, the reaction analysis operation unit 24 reads
the pieces of reaction information stored in the reaction analysis
storage unit 23 in turn and performs a predetermined data analysis
on each read reaction information. For example, the reaction
analysis operation unit 24 reads, from the reaction analysis
storage unit 23, information indicating a description "I am
brokenhearted . . ." inputted into a dairy or a voice "I am sad"
made by the user when listening to a tune A or information
indicating the user's body heat, blood pressure, or heart rate at
the time when the user's energy is increased by listening to the
tune A, performs a data analysis on such information, and obtains a
reaction analysis result in the form of text data.
[0082] In step S24, the reaction analysis operation unit 24
provides the obtained reaction analysis result to the information
integration unit 16. For example, the reaction analysis operation
unit 24 provides, to the information integration unit 16, text data
indicating "I am heartbroken" or "I am sad," or "an increase in
energy" set on the basis of the value of the body heat, blood
pressure, or heart rate, obtained as the result of the data
analysis.
[0083] Referring back to FIG. 2, in step S12, the environment
analysis unit 12 performs an environment analysis process, more
specifically, performs a predetermined data analysis on environment
information about the date and time, place, temperature, or the
like at the time when the user views and listens to content and
user's other surroundings.
[0084] Environment Analysis Process by Reaction Analysis Unit
[0085] Referring now to FIG. 4, an environment analysis process
that the reaction analysis unit 12 performs will be described.
[0086] In step S31, the environment analysis detection unit 31
detects or determines, in real time, environment information about
the date and time, place, temperature, season, current topics,
fashions, or the like at the time when the user views and listens
to content under the control of the environment analysis operation
unit 33 and provides the environment information to the environment
analysis storage unit 32.
[0087] In step S32, the environment analysis storage unit 32
chronologically stores pieces of environment information provided
by the environment analysis detection unit 31.
[0088] In step S33, the environment analysis operation unit 33
reads the pieces of environment information stored in the
environment analysis storage unit 32 in turn and performs a
predetermined data analysis on each read environment information.
For example, the environment analysis operation unit 33 reads, from
the environment analysis storage unit 32, positional information
indicating the position of a convenience store to which the user
went while listening to the tune A or information indicating
"healing," a vogue word in those days, performs a data analysis on
such information, and obtains an environment analysis result in the
form of text data.
[0089] In step S34, the environment analysis operation unit 33
provides the obtained environment analysis result to the
information integration unit 16. For example, the environment
analysis operation unit 33 provides, to the information integration
unit 16, text data indicating positional information "convenience
store" or a vogue word "healing" obtained as the result of the data
analysis.
[0090] Referring back to FIG. 2, in step S13, the signal analysis
unit 13 performs a signal analysis process, more specifically,
performs a signal analysis on image signals and voice signals of
content.
[0091] Signal Analysis Process by Signal Analysis Unit
[0092] Referring now to a flowchart of FIG. 5, a signal analysis
process that the signal analysis unit 13 performs will be
described.
[0093] In step S41, the signal analysis operation unit 42 reads
content stored in the signal analysis storage unit 41 and extracts
image signals and voice signals from the content.
[0094] In step S42, the signal analysis operation unit 42 performs
a signal analysis, such as a feature quantity analysis, on the
image signals and voice signals of the content read from the signal
analysis storage unit 41. For example, the signal analysis
operation unit 42 performs a signal analysis on voice signals of
the read content, tune A, and obtains an analysis result in the
form of a feature quantity.
[0095] In step S43, the signal analysis operation unit 42 provides
the signal analysis result to the information integration unit 16.
For example, the signal analysis operation unit 42 provides, to the
information integration unit 16, moods ("sad," "healing,"
"refreshing," "romantic," "hopeful," etc.), genres ("pop," "rock,"
"jazz," etc.), categories ("tune," "movie," etc.), and the like,
which are feature quantities obtained as the signal analysis
result.
[0096] Referring back to FIG. 2, in step S14, the language analysis
unit 14 performs a language analysis process, more specifically,
performs a language analysis on language information about
content.
[0097] Language Analysis Process by language Analysis Unit
[0098] Referring now to a flowchart of FIG. 6, a language analysis
process that the language analysis unit 14 performs will be
described.
[0099] In step S51, the language analysis operation unit 52 reads
content stored in the language analysis storage unit 51 and
extracts, from the content, language information, such as sentences
or words described about the content.
[0100] In step S52, the language analysis operation unit 52
performs a language analysis, such as a morphological analysis, on
the language information about the content read from the language
analysis storage unit 51. For example, the language analysis
operation unit 52 performs a language analysis on the extracted
language information, the lyrics of the tune A, and obtains text
data ("broken heart," "convenient store," "vigorous," "Sunday,"
"phone message, etc.), which are lyrics phrases, as the analysis
result.
[0101] In step S53, the language analysis operation unit 52
provides the language analysis result to the information
integration unit 16. For example, the language analysis operation
unit 52 provides, to the information integration unit 16, the text
data, which are lyrics phrases of the tune A, obtained as the
language analysis result.
[0102] Referring back to FIG. 2, in step S15, the information
integration unit 16 performs an information integration process,
more specifically, integrates the analysis results from the
reaction analysis unit 11, environment analysis unit 12, signal
analysis unit 13, and language analysis unit 14.
[0103] Information Integration Process by Information Integration
Unit
[0104] Referring now to a flowchart of FIG. 7, an information
integration process that the information integration unit 16
performs will be described.
[0105] In step S71, the relationship degree calculation unit 71a of
the information integration control unit 71 calculates the
experience relationship degree among the reaction analysis result
from the reaction analysis unit 11, the environment analysis result
from the environment analysis unit 12, the signal analysis result
from the signal analysis unit 13, and the language analysis result
from the language analysis unit. For example, the relationship
degree calculation unit 71a calculates the experience relationship
degree by obtaining the signal or language correlation or
similarity between the user situation analysis result (reaction
analysis result and environment analysis result) and the content
analysis result (signal analysis result and language analysis
result).
[0106] More specifically, the relationship degree calculation unit
71a calculates a latent (statistic process basis) or semantic
(dictionary basis) experience relationship degree by performing a
feature quantity analysis technology or language analysis
technology on the user situation analysis result and content
analysis result.
[0107] Examples of the feature quantity analysis technology include
a technique that obtains the correlation between context indicating
the situation of the user and the feature quantity of content by
mapping the context and the feature quantity on the same plane, as
described in, for example, Japanese Unexamined Patent Application
Publication No. 2007-172523. Also, a technique may be used to add a
cluster corresponding to a mood obtained through learning to a tune
having a feature quantity not corresponding to the user's feeling
(mood), as described in, for example, Japanese Unexamined Patent
Application Publication No. 2007-207218.
[0108] For example, the relationship degree calculation unit 71a
obtains the correlation between the reaction analysis result, text
data "I am sad," and the signal analysis result, mood "sad," which
a feature quantity, and thus calculates an experience relationship
degree C.sub.1=1.0.
[0109] Also, the relationship degree calculation unit 71a obtains
the correlation between the environment analysis result, text data
"healing," and the signal analysis result, mood "healing," which is
a feature quantity, and thus calculates an experience relationship
degree C.sub.2=1.0.
[0110] Examples of the language analysis technology include a
technique that creates analysis targets on the same space latently
using probabilistic latent semantic analysis (PLSA), as described
in, for example, Japanese Unexamined Patent Application Publication
No. 2007-241888.
[0111] For example, the relationship degree calculation unit 71a
obtains the latent similarity between the reaction analysis result,
text data "I am broken hearted," and the language analysis result,
text data "broken heart," which is a lyrics phrase, and thus
calculates an experience relationship degree C.sub.3=0.8.
[0112] The relationship degree calculation unit 71a also obtains
the latent similarity between the environment analysis result, text
data "convenience store," and the language analysis result, text
data "convenience store," which is a lyrics phrase, and thus
calculates an experience relationship degree C.sub.4=1.0.
[0113] The relationship degree calculation unit 71a also obtains
the latent similarity between the reaction analysis result, text
data "an increase in energy," and the language analysis result,
text data "vigorous," which is a lyrics phrase, and thus calculates
an experience relationship degree C.sub.5=0.9.
[0114] The feature quantity analysis technology and language
analysis technology may be other than the above-mentioned ones.
[0115] In this way, the relationship degree calculation unit 71a
calculates an experience relationship degree indicating the
relationship between information about the user's reaction or
surroundings at the time when the user views and listens to content
and information about the content itself on the basis of the signal
or language correlation between the user situation analysis result
and content analysis result. The association degree calculation
unit 71a may calculate an experience relationship degree between
the reaction analysis result and environment analysis result or
between the signal analysis result and language analysis result,
which is not mentioned above. In other words, the relationship
degree calculation unit 71a may calculate an experience
relationship degree between any two of the reaction analysis
result, environment analysis result, signal analysis result, and
language analysis result.
[0116] In step S72, the importance degree calculation unit 71b
performs a predetermined operation on the experience relationship
degree calculated by the association degree calculation unit 71a so
as to calculate the importance degree of a content experience. For
example, the importance degree calculation unit 71b sums up the
experience relationship degrees C1 and C5 and thus obtains an
experience importance degree I=4.7(=1.0+1.0+0.8+1.0+0.9). Also, for
example, considering the experience relationship degrees as
vectors, the importance degree calculation unit 71b may obtain the
sum of the cosine distances between the vectors as the experience
importance degree. The cosine distance here refers to a value
obtained by dividing the inner product of two vectors by the
product of the absolute values of the vectors. The experience
importance degree may be obtained using operation techniques other
than the above-mentioned technique.
[0117] The importance degree calculation unit 71b provides, to the
information integration storage unit 72, the calculated importance
degree of the content experience, the name of the experienced
content, and text data (hereafter referred to as a "keyword")
corresponding to the content analysis result used to calculate the
experience relationship degree in such a manner that these items
are associated with one another. For example, the importance degree
calculation unit 71b provides, to the information integration
storage unit 72, an experience importance degree "4.7" about the
tune A, the content name "tune A," and text data("sad," "healing,"
"refreshing," etc. and "broken heart," "convenience store,"
"vigor," etc.) corresponding to the content analysis result (signal
analysis result and language analysis result) in such a manner that
these items are associated with one another.
[0118] In step S73, the information integration storage unit 72
stores the experience importance degree from the information
integration control unit 71.
[0119] The above-mentioned process allows calculating the
experience importance degree with respect to viewing and listening
of content by the user on the basis of the relationship between the
situations of the user himself/herself and user's surroundings at
the time when the user views and listens to the content and the
feature quantity of the content itself, such as a mood, or language
information such as lyrics.
[0120] Described above is the process of calculating the importance
degree of content experience on the basis of the situation of the
user at the time when the user experiences the content. Next, there
will be described a process of, in cases such as one where the user
newly experiences content, recommending content optimum for the
user at that time on the basis of previously calculated experience
importance degrees.
[0121] Content Recommendation Process by Information Processing
System
[0122] Referring now to a flowchart of FIG. 8, a content
recommendation process that the information processing system of
FIG. 1 performs will be described. The content recommendation
process is performed when the user input unit 81 receives, for
example, an operation for obtaining a content recommendation.
[0123] In step S91, the content experience operation unit 82
determines whether the user has inputted a search keyword for
retrieving content to be viewed and listened to by operating the
user input unit 81.
[0124] If it is determined in step S91 that the search keyword has
been inputted, the content experience operation unit 82 obtains the
search keyword in step S92 and provides the search keyword to the
retrieval unit 92a of the content recommendation operation unit 92
via the information integration management unit 71.
[0125] In contract, if it is determined in step S91 that no search
keyword has been inputted, step S92 is skipped and the process
proceeds to step S93.
[0126] In step S93, the content experience operation unit 82
determines whether, when operating the user input unit 81, the user
has instructed the information processing system to perform a
reaction analysis process.
[0127] If it is determined in step S93 that the information
processing system has been instructed to perform a reaction
analysis process, the content experience operation unit 82, in step
S94, causes the reaction analysis operation unit 24 to perform a
reaction analysis process, via the information integration control
unit 71. Step S94 is the same as step S24 of the flowchart of FIG.
3 described above except that the analysis result is provided to
the retrieval unit 92a via the information integration unit 16 and
therefore will not be described.
[0128] Note that, in step S94, the reaction analysis unit 11 does
not handle information corresponding to the reaction of the user at
the time when the user views and listens to content but handles, as
reaction information, for example, information corresponding to an
email or diary inputted by the user or a description made by the
user on a web page via the Internet before the user views and
listens to the content.
[0129] In contract, if it is determined in step S93 that the
information processing system has not been instructed to perform a
reaction analysis process, step S94 is skipped and the process
proceeds to step S95.
[0130] In step S95, the content experience operation unit 82
determines whether, when operating the user input unit 81, the user
has instructed the information processing system to perform an
environment analysis process.
[0131] If it is determined in step S95 that the information
processing system has been instructed to perform an environment
analysis process, the content experience operation unit 82, in step
S96, causes the environment analysis operation unit 33 to perform
an environment analysis process, via the information integration
control unit 71. Step S96 is the same as step S34 of the flowchart
of FIG. 4 described above except that the analysis result is
provided to the retrieval unit 92a via the information integration
unit 16 and therefore will not be described.
[0132] Note that, in step S96, the environment analysis unit 12
does not handle information about the environment at the time when
the user views and listens to content but handles, as environment
information, information about the user's existing place, the
temperature thereof, and user's other surroundings before the user
views and listens to the content.
[0133] In contract, if it is determined in step S95 that the
information processing system has not been instructed to perform an
environment analysis process, step S96 is skipped and the process
proceeds to step S97.
[0134] In step S97, the retrieval unit 92a retrieves content to be
recommended on the basis of at least one of the search keyword,
reaction analysis result, and environment analysis result. For
example, the retrieval unit 92a retrieves an experience importance
degree corresponding to a keyword matching the search keyword from
among the experience importance degrees stored in the content
recommendation storage unit 91. For example, if the user inputs a
search keyword "sad," the retrieval unit 92a retrieves an
experience importance degree corresponding to the keyword "sad" and
determines content corresponding to the retrieved experience
importance degree, as content to be recommended (recommendation
candidate).
[0135] FIG. 9 shows an example of the list of pieces of content
retrieved by the retrieval unit 92a. In FIG. 9, with the title
(name) of each tune (content), are associated a genre, lyrics
phrases serving as keywords, moods, and an experience importance
degree. The genre is one of pieces of meta-data about the content
(tune). Other items may be used instead of the genre. For example,
the retrieval unit 92a may retrieve content using the genre as a
keyword.
[0136] In FIG. 9, for example, with the tune A, are associated a
genre "pop," pieces of text data "broken heart (0.8)," "convenience
store (1.0)," "vigor (0.9)," "Sunday," "phone message," and the
like, which are lyrics phrases, moods "sad (1.0)," "healing (1.0),"
"refreshing," "romantic," and "hopeful", and an experience
importance degree "4.7." In the drawing, the parenthesized values
as seen in "broken heart (0.8)" represent the experience
relationship degrees calculated to obtain the experience importance
degrees. With a tune B, are associated a genre "rock," keywords
"betrayal," "comfort," "pain," "dawn (0.3), " hunch," and the like,
which are lyrics phrases, moods "sad (0.8)," "sentimental,"
"frail," "gloomy," and "uneasy (1.0)," and an experience importance
degree "2.1." Likewise, with tunes C to J, are associated genres,
words in the lyrics, moods, and experience importance degrees.
[0137] As shown in FIG. 9, with any tune, is associated the keyword
"sad" indicating a mood. That is, if the user inputs a search
keyword "sad," the retrieval unit 92a determines the ten pieces of
content shown in FIG. 9, the tunes A to J, as pieces of content to
be recommended (recommendation candidates).
[0138] Referring back to the flowchart of FIG. 8, in step S98, the
sorting unit 92b sorts pieces of content retrieved by the retrieval
unit 92a, according to the corresponding experience importance
degrees. For example, the sorting unit 92b sorts the tune A to J
shown in FIG. 9 in the descending order of experience importance
degrees. More specifically, the sorting unit 92b sorts the tune A
to J shown in FIG. 9 in the order of the tunes A, E, C, I, H, J, D,
B, G, and F. If multiple pieces of content have the same experience
importance degree, for example, a piece of content experienced by
the user more recently thereof may be sorted in an upper place.
[0139] In step S99, the determination unit 92c determines a piece
of content having a high experience importance degree, that is, a
piece of content corresponding to an episode among the pieces of
content sorted by the sorting unit 92b, as recommendation content
to be recommended to the user. The determination unit 92c provides
a content recommendation (e.g., the name of recommendation content)
to the content experience operation unit 82 via the information
integration unit 16. For example, the determination unit 92c
determines, as recommendation content, pieces of content having a
content experience importance degree higher than a predetermined
threshold (e.g., 20) among the pieces of content sorted in the
order of the tunes A, E, C, I, H, J, D, B, G, and F. In this case,
eight pieces of content, tunes A, E, C, I, H, J, D, and B, are
determined as recommendation content. The determination unit 92c
provides a content recommendation to the content experience
operation unit 82 via the information integration unit 16. At that
time, along with the content recommendation, the determination unit
92c may provide, as meta-data, those having high experience
relationship degrees among the lyrics phrases and moods (keywords)
associated with the pieces of recommendation content to the content
experience operation unit 82.
[0140] For example, in step S99, the determination unit 92c may
determine, as recommendation content, pieces of content
corresponding to the highest five content experience importance
degrees among the pieces of content sorted in the order of the
tunes A, E, C, I, H, J, D, B, G, and F. In this case, five pieces
of content, tunes A, E, C, I, and H, are determined as
recommendation content. The number of the highest content
experience importance degrees may be a number other than five.
[0141] In step S100, the content experience operation unit 82
causes the content experience display unit 84 to display the
content recommendation (the name of recommendation content)
provided by the determination unit 92c via the information
integration unit 16. If those having high experience relationship
degrees among the keywords associated with the recommendation
content are provided as meta-data by the determination unit 92c,
the keywords may be displayed as meta-data along with the content
recommendation.
[0142] At that time, in order to remind the user of an episode, the
content experience operation unit 82 may read information
corresponding to the reaction of the user or the environment at the
time when the user experienced the recommendation content in the
past, from the reaction analysis storage unit 23 or environment
analysis storage unit 32 via the information integration unit 16.
For example, the content experience operation unit 82 may cause the
content experience display unit 84 to indicate that the
recommendation content is the tune A, as well as to display
sentences such as "I went to a "convenience store" while listening
to the tune A," ""healing" was a vogue word in those days," and "I
listened to the tune A and murmured, "I am sad.""
[0143] The above-mentioned process allows, in a case when the user
newly experiences content, recommending content on the basis of an
experience importance degree calculated on the basis of the
relationship between the situations of the user himself/herself and
the user's surroundings at the time when the user viewed and
listened content in the past and the feature quantity of the
content itself, such as a mood, or language information such as
lyrics. This allows recommending optimum content corresponding to
the situation of the user.
[0144] The recommended content is content corresponding to a
content experience having a high experience importance degree, that
is, content corresponding to an episode that was particularly
impressive for the user. Accordingly, the recommended content can
remind the user of the impressive past memory.
[0145] Described above is the example where content is retrieved on
the basis of a search keyword in step S97. On the other hand, in a
case where content is retrieved on the basis of a reaction analysis
result or environment analysis result, the retrieval unit 92a
retrieves content whose number of keywords matching keywords
corresponding to the analysis result is larger than a predetermined
number. Also, in a case where content is retrieved on the basis of
any two or all of a search keyword, a reaction analysis result, and
an environment analysis result, content meeting these conditions
will be retrieved.
[0146] Also, in a case where the retrieval unit 92a retrieves
content by binarizing a feeling of the user on the basis of a
reaction analysis result and an environment analysis result, that
is, a user situation analysis result before the user newly views
and listens to the content, the retrieval unit 92a may retrieve
content on the basis of corresponding preference information and
the binarized user feeling.
[0147] The content recommendation process of FIG. 8 has been
described assuming that the process is performed in a case where
the user newly views and listens to content. On the other hand, if
no search keyword acquisition process is performed in steps S91 and
S92, the content recommendation process may be performed not only
in a case where the user newly views and listens to content but
also, for example on the basis of a user situation analysis result
obtained in the daily life of the user. This allows recommending
content corresponding to the situations of the user himself or
herself and user's surroundings in the daily life without the user
having to perform an operation for requesting recommendation
content.
[0148] The above-mentioned series of processes may be performed by
any of hardware and software. In a case where the series of
processes are performed by software, a program constituting the
software is installed from a storage medium into a computer
included in dedicated hardware, a general-purpose personal computer
that can perform various functions by having various programs
installed therein, or the like.
[0149] FIG. 10 shows an example configuration of such a
general-purpose computer. This personal computer includes a central
processing unit (CPU) 901. Connected to the CPU 901 via a bus 904
is an input/output interface 905. Connected to the bus 904 are a
read only memory (ROM) 902 and a random access memory (RAM)
903.
[0150] Connected to the input/output interface 905 are an input
unit 906 composed of input devices by which the user inputs
operation commands, such as a keyword and mouse, an output unit 907
that outputs a process operation screen or a process result image
to a display device, a storage unit 908 composed of a hard disk
drive or the like that stores programs and various types of data,
and a communication unit 909 that is composed of a local area
network (LAN) adapter and performs communication processes via a
network typified by the Internet. Also connected to the
input/output interface 905 is a drive 910 that reads or writes data
from or to a removable medium 911 such as a magnetic disk (e.g., a
flexible disk), optical disk (e.g., a compact disc read-only memory
(CD-ROM) or digital versatile disc (DVD)), optical magnetic disc
(e.g., Mini Disc (MD)), or semiconductor memory.
[0151] The CPU 901 performs various processes in accordance with
programs stored in the ROM 902 or programs that are read from the
removable medium 911, installed into the storage unit 908, and
loaded from the storage unit 908 into the RAM 903. Data or the like
necessary for the CPU 901 to perform various processes is stored in
the RAM 903, if necessary.
[0152] In this specification, the step of writing a program to be
stored in a storage medium includes not only processes that are
performed chronologically according to the written order but also
processes that are not typically performed chronologically but
performed simultaneously or separately.
[0153] In this specification, the system represents a whole
apparatus including multiple apparatuses or processing units. In
other words, the information processing system of FIG. 1 may be
composed of a single apparatus such as the personal computer of
FIG. 10 or construed as such an apparatus.
[0154] The present application contains subject matter related to
that disclosed in Japanese Priority Patent Application JP
2009-112016 filed in the Japan Patent Office on May 1, 2009, the
entire content of which is hereby incorporated by reference.
[0155] It should be understood by those skilled in the art that
various modifications, combinations, sub-combinations and
alterations may occur depending on design requirements and other
factors insofar as they are within the scope of the appended claims
or the equivalents thereof.
* * * * *